Consensys gnark-crypto through 0.11.2 allows Signature Malleability. This occurs because deserialisation of EdDSA and ECDSA signatures does not ensure that the data is in a certain interval.

gnark is a zk-SNARK library that offers a high-level API to design circuits. Prior to version 0.9.0, for some in-circuit values, it is possible to construct two valid decomposition to bits. In addition to the canonical decomposition of `a`, for small values there exists a second decomposition for `a+r` (where `r` is the modulus the values are being reduced by). The second decomposition was possible due to overflowing the field where the values are defined. Upgrading to version 0.9.0 should fix the issue without needing to change the calls to value comparison methods.

Consensys Discovery versions less than 0.4.5 uses the same AES/GCM nonce for the entire session. which should ideally be unique for every message. The node's private key isn't compromised, only the session key generated for specific peer communication is exposed.

gnark is a fast zk-SNARK library that offers a high-level API to design circuits. In gnark 0.11.0 and earlier, deserialization of Groth16 verification keys allocate excessive memory, consuming a lot of resources and triggering a crash with the error fatal error: runtime: out of memory.

gnark is a zero-knowledge proof system framework. In versions prior to 0.14.0, the Verify function in eddsa.go and ecdsa.go used the S value from a signature without asserting that 0 ≤ S < order, leading to a signature malleability vulnerability. Because gnark’s native EdDSA and ECDSA circuits lack essential constraints, multiple distinct witnesses can satisfy the same public inputs. In protocols where nullifiers or anti-replay checks are derived from R and S, this enables signature malleability and may allow double spending. This issue has been addressed in version 0.14.0.

gnark is a zero-knowledge proof system framework. In version 0.12.0, there is a potential denial of service vulnerability when computing scalar multiplication is using the fake-GLV algorithm. This is because the algorithm didn't converge quickly enough for some of the inputs. This issue has been patched in version 0.13.0.